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User Recommendations in Reciprocal and
Bipartite Social NetworksAn online Dating Case Study
Presented by: Md. Kamruzzaman
Authors
Kang Zhao and Xi Wang, University of Iowa
Mo yu, Penn State University
Bo Gao, Beijing Jiaotong University
Social Network
• Facebook, LinkedIn,Twitter.• Billions of user.• Huge revenue opportunity.• Vast impact on social life.
Reciprocal & Bipartite Network
• Online Dating Site, Matrimony Site.• Both reciprocal and bipartite network exist.
Goal
• Authors take online dating site for the research.• Better user recommendation is the ultimate goal.
User Recommendation
• Freinds of friends is the most popular method.• FoF is not possible for Reciprocal – bipartite network.• Existing system deals with explicit profile. Such as profession, age.• In this method, authors tried to find out a new way other than using
explicit profile.
Calculation
• User, p• Target user, t
Using this formula given a success score for target user, the higher the score, the higher probability of recommendation.
Evaluation
• Two sets of metrics to evaluate• Initial contact
-IC precision@k-IC recall@k
• Reciprocal contact-RC precision@k-RC recall@k
Final Words
• According to experiment, Hybrid Model is more successful approach.
• Can be applied in university admission network, job hunting network.
• Use of sensitivity analysis can make HM more robust.
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